PINN-based computational framework for femtosecond laser ablation: multi-material modeling and parameter estimation

Purpose: For more than 30 years, robust parameter design (RPD), which attempts to minimize the process bias (i.e., deviation between the mean and the target) and its variability simultaneously, has received consistent attention from researchers in academia and industry. Based on Taguchi’s philosophy, a number of RPD methodologies have been developed to improve the quality of products and processes. The primary purpose of this paper is to review and discuss existing RPD methodologies in terms of the three sequential RPD procedures of experimental design, parameter estimation, and optimization.Methods: This literature study composes three review aspects including experimental design, estimation modeling, and optimization methods.Results: To analyze the benefits and weaknesses of conventional RPD methods and investigate the requirements of future research, we first analyze a variety of experimental formats associated with input control and noise factors, output responses and replication, and estimation approaches. Secondly, existing estimation methods are categorized according to their implementation of least-squares, maximum likelihood estimation, generalized linear models, Bayesian techniques, or the response surface methodology. Thirdly, optimization models for single and multiple responses problems are analyzed within their historical and functional framework.Conclusion: This study identifies the current RPD foundations and unresolved problems, including ample discussion of further directions of study.

Aerodynamic Modeling and Parameter Estimation from QAR Data of an Airplane Approaching a High-altitude Airport

A fast inverse heat conduction model (IHCM) is developed for estimating unknown properties of multi-layer structures considering internal heating. The model leverages a closed-form analytical forward solution to enable efficient inverse computations. It requires only a single internal temperature measurement as input, with unknown parameters estimated by minimizing an objective function using an interior-point optimization algorithm. The IHCM accurately identifies thermal properties such as thermal conductivity, specific heat capacity, density, and heat transfer coefficient in a high-precision linear motor. It also detects internal geometric variations, including the location and severity of delamination caused by thermal expansion. These predictions are validated against finite element (FE) simulations. Furthermore, a sensorless strategy is proposed, enabling non-invasive parameter estimation based on electrically inferred temperature data. The feasibility, sensitivity, and limitations of the IHCM are assessed across various scenarios. Results demonstrate its strong potential for real-time diagnostics, online defect detection, and thermal performance monitoring in multi-layer composite systems with internal heat generation, such as electrical machines. • Fast inverse parameter estimation in multilayer composites with internal heating. • Diagnoses thermal aging and detects global and local delamination. • Flexible analytical forward framework for multiple non-homogeneities. • High computational speed enables real-time, lightweight thermal diagnosis. • Noninvasive, sensorless estimation through electrically inferred temperature.

Summary: Here, we present REDEMPTION (Reduced Dimension Ensemble Modeling and Parameter estimation), a toolbox for parameter estimation and ensemble modeling of ordinary differential equations (ODEs) using time-series data. For models with more reactions than measured species, a common scenario in biological modeling, the parameter estimation is formulated as a nested optimization problem based on incremental parameter estimation strategy. REDEMPTION also includes a tool for the identification of an ensemble of parameter combinations that provide satisfactory goodness-of-fit to the data. The functionalities of REDEMPTION are accessible through a MATLAB user interface (UI), as well as through programming script. For computational speed-up, REDEMPTION provides a numerical parallelization option using MATLAB Parallel Computing toolbox.Availability and implementation: REDEMPTION can be downloaded from http://www.cabsel.ethz.ch/tools/redemption.Contact:rudi.gunawan@chem.ethz.ch

Degradation Modeling and RUL Estimation of Deteriorating Systems in S-Plane

The damage of the novel Coronavirus disease (COVID-19) is reaching an unprecedented scale. There are numerous classical epidemiology models trying to quantify epidemiology metrics. To forecast epidemics, classical approaches usually need parameter estimations, such as the contagion rate or the basic reproduction number. Here, we propose a data-driven, parameter-free, geometric approach to access the emergence of a pandemic state by studying the Forman–Ricci and Ollivier–Ricci network curvatures. Discrete Ollivier–Ricci curvature has been used successfully to forecast risk in financial networks and we suggest that those results can provide analogous results for COVID-19 epidemic time-series. We first compute both curvatures in a toy-model of epidemic time-series with delays, which allows us to create epidemic networks. We also compared our results to classical network metrics. By doing so, we are able to verify that the Ollivier–Ricci and Forman–Ricci curvatures can be a parameter-free estimate for identifying a pandemic state in the simulated epidemic. On this basis, we then compute both Forman–Ricci and Ollivier–Ricci curvatures for real epidemic networks built from COVID-19 epidemic time-series available at the World Health Organization (WHO). This approach allows us to detect early warning signs of the emergence of the pandemic. The advantage of our method lies in providing an early geometrical data marker for the pandemic state, regardless of parameter estimation and stochastic modelling. This work opens the possibility of using discrete geometry to study epidemic networks.

IntroductionModern exoplanetary research increasingly requires combining expertise from astronomy, data science, and research software engineering. The complexity and volume of data from current and future missions demand not only robust processing techniques, but also transparent and reproducible analysis frameworks. In this context, we present a modular, open-source software framework developed to support systematic comparison of different data processing and modelling methods. Its architecture allows for flexibility, extensibility, and collaboration across disciplines.The primary aim of this tool is to improve the reliability and interpretability of data-intensive exoplanet analysis workflows. By enabling the side-by-side application of multiple methodologies, researchers can better understand how specific assumptions and preprocessing decisions affect the outcomes of scientific inference.MethodThe framework allows researchers to create interchangeable modules covering stages such as data preparation, detrending, modelling, and parameter estimation. Each module is designed to operate independently within a defined interface, allowing researchers to substitute components and evaluate their effects on final results.This design supports collaborative development and integration of methods by contributors with varied technical backgrounds. It facilitates comparative workflows and improves reproducibility by preserving the full sequence of processing steps used in each analysis.As a proof of concept, the framework has been applied to the analysis of the long-period exoplanet candidate TOI-4409 b. This target was observed across 21 transits using a combination of ground-based and space-based photometric instruments, including TESS, ASTEP, CHAT, OMES, LCOGT, and PEST. The tool was used to integrate the datasets and test multiple modelling approaches in order to refine the planet’s properties. Additionally, the framework is being used in the analysis of archival TESS survey data to explore the effects of different noise-processing strategies on large-scale population-level studies.ResultsThe application of the framework to TOI-4409 b demonstrated its capacity to manage multi-instrument datasets and compare model outputs under controlled conditions. The combined light curve analysis yielded updated estimates of the planet’s physical and orbital parameters, building on previos findings.In parallel, its use in TESS archive analysis highlights the framework’s scalability and utility in benchmarking data-processing techniques across large datasets, allowing users to evaluate the impact of algorithmic variation on statistical outcomes.ConclusionThis modular framework contributes to current efforts in research software engineering by providing a reproducible and extensible platform for astronomical data analysis. Its structure supports interdisciplinary collaboration by enabling clear communication and integration of alternative methodologies.By presenting this tool within the EPSC-DPS community, particularly in the context of cross-disciplinary planetary science collaboration, we aim to introduce it as a useful addition to researchers' analytical toolkits, and to gather practical feedback from a diverse range of users to guide its development and ensure it aligns with real scientific needs.

I. INTRODUCTIONIn order to achieve reliable and high-performance control of permanent magnet synchronous machines (PMSMs), the accurate model of electromagnetic characteristics of PMSMs under different load conditions is critical [1]. The conventional method is to use the finite element analysis (FEA) to derive the magnetic model of PMSM [2]. However, this method requires accurate geometric parameters and material information of the machine and the results must be validated by experiments. Therefore, it is desirable to obtain accurate machine model from measurable terminal signals.To date, considerable research work has been conducted to deal with the flux linkage identification problem for PMSMs [3]-[5]. However, either magnetic saturation or spatial harmonics effects have been ignored during the flux linkage identification in the existing methods. Therefore, this paper proposes a novel two-step flux linkage identification method for PMSMs considering both magnetic saturation and spatial harmonics. Step one is an offline estimation of the flux linkages considering magnetic saturation based on genetic algorithm (GA) , while the spatial harmonics effect has been mitigated compared with the existing method. In the second step, a state-space observer is employed to estimate the spatial harmonics online with the estimated offline data in step one. In addition, Kalman filter is applied in dq-axis inductances harmonics estimation to reduce the influence of noise and high order harmonics in the motor drive system. Therefore, the proposed method achieves to consider both magnetic saturation and spatial harmonics for PMSM flux linkage identification. The proposed approach is validated on both the surface-mounted and interior PMSMs.II. Magnetic Saturation Modeling and Parameter Estimation using GATo consider magnetic saturation in the machine model, the dq-axis flux linkages are modeled using the quadratic equation as in (1).Ψd(id, iq)= α0+α1id+α2iq+α3id2+α4iq2+α5idiq;Ψq(id, iq)= β0+β1id+β2iq+β3id2+β4iq2+β5idiq. (1)Where id, iq, Ψd, Ψq are dq-axis currents and flux linkages; α0 denotes the fundamental PM flux linkage; β0 is adopted to generalize the quadratic form of the model and compensate the offset for Ψq; α1 and β1 denote self-inductances in the dq-axis; α2,…, α5 and β2,…, β5 denote the cross-coupling effect and the influence of magnetic saturation. There are 12 parameters to be estimated, while the rank number is two. Therefore, the coefficients of flux linkages equations cannot be solved directly as it is rank-deficient. To solve this issue, a GA based estimation approach is proposed.Suppose that, for a given pair of (id, iq), the (ud, uq) under a certain speed ωm during the period T seconds (T=2πn/ωm, where n=1,2, …,N represents the number of rotations) are recorded in order to mitigate the influence of spatial harmonics. To estimate the coefficients of flux linkages, two fitness functions are designed as follows:Fd = ∑i=1..I∑j=1..J[α0 + α1id(i) + α2iq(j) + α3id2(i) +α4iq2(j) + α5id(i)iq(j)-Ψd(id(i), iq(j))];Fq = ∑i=1..I∑j=1..J[β0 + β1id(i) + β2iq(j) + β3id2(i) +β4iq2(j) + β5id(i)iq(j)-Ψq(id(i), iq(j))], (2)where i=1,…, I and j=1,…, J are directories of (id, iq). Based on (2), the coefficients of dq-axis flux linkages at each pair of (id, iq) can be obtained by using GA to achieve the minimization of Fd and Fq.III. Online Parameter Estimation Considering Spatial HarmonicsConsidering spatial harmonics, dq-axis voltage equations of a PMSM can be written as (3) under the steady state.ud = Rid-ωeLqq,0iq+ωeΨd6sin(6pθ)+sin(6pθ)(-6pωeLdd,6id-ωeLdq,6id)+cos(6pθ)(6pωeLdq,6iq-ωeLqq,6iq)+εd ; uq = Riq+ωeLdd,0id+ωeΨd1+ωeΨq6cos(6pθ)+sin(6pθ)(-6pωeLqq,6iq-ωeLdq,6iq)+cos(6pθ)(6pωeLdq,6id-ωeLdd,6id)+εq, (3)where ud, uq are dq-axis voltages; θ is the electrical rotor position; p is the number of pole pairs; R is the resistance; ωe is the rotor speed; Ψd6 and Ψq6 are the magnitudes of the 6th permanent magnet (PM) flux-linkage harmonics in dq-axis. Ldd,0,Lqq,0,Ldd,6,Lqq,6 are the magnitudes of fundamental and the 6th harmonic of self-inductances in the dq-axis; Ldq,6 is the magnitude of the 6th harmonic of mutual-inductance; εd and εq are high order harmonics and high frequency noise components in the dq-axis. The harmonic magnitudes of the PM flux linkage can be easily obtained by using FFT analysis of the back-electromotive force. To achieve an online identification of the dq-axis inductances harmonics, a state observer is employed.The state observer model is firstly built based on (3) to get the magnitude of the 6th harmonic of the self-inductance and mutual-inductance in the dq-axis. The state vector is built as:x=[ Ls6cos(6pθ); Lm6sin(6pθ); -6pωeLs6sin(6pθ); 6pωeLm6cos(6pθ)], (4)with Ls6=Ldd,6 and Lm6=Ldq,6. The estimated dq-axis flux linkage maps are shown in Fig.1 and Fig.2. Detail analysis and experimental results will be given in the full paper.IV.CONCLUSIONThis paper proposes a novel two-step method to identify flux linkages in PMSMs considering both the effects of magnetic saturation and spatial harmonics during the estimation process. In step one, GA is used to estimate the magnetic saturation coefficients of flux linkage. With the results obtained from GA, the Kalman filter based online method is proposed to estimate the spatial harmonics to achieve an accurate estimation of the flux linkage map. The co-simulation results show that the proposed approach is effective for flux linkage estimation. **

Review of <i>Environmental and Hydrological Systems Modeling</i> by A. W. JayawardenaCRC Press Taylor &amp; Francis Group, Boca Raton, FL 33487; 2014; ISBN 978-0-415-46532-8; 516 pp.; $99.95.

This work introduces a metaheuristic (MH) optimization method, which is inspired by the red-tailed hawks' predatory behavior is Improved Red-tailed Hawk (IRTHA) Algorithm. The algorithm uses a dynamic adjustment method which uses the combined effect of nonlinear decay and chaotic mapping to enhance the convergence efficacy and accuracy of outcomes. This enhancement affects the search radius of the algorithm and creates diversity in the dive speed of hawks, hence adaptively balancing exploration and exploitation, enhancing diversity and convergence. IRTHA's efficacy is examined for single, double, and triple diode models of various photovoltaic (PV) cells and modules, such as RTC France, PVM 752, STP 120/36, STM 40/36, and Photowatt-PWP201. A comparative analysis of IRTHA with other advanced MH optimization techniques indicates that IRTHA exhibits considerably lower RMSE values: 7.72986E-04 for SDM-RTC France, 7.41918E-04 for DDM-RTC France, 7.34782E-04 for TDM-RTC France, 1.59243E-04 for PVM 752, 1.44508E-02 for STP 120/36, 1.72192E-03 for STM 40/36, and 2.05285E-03 for the Photowatt-PWP201 module, respectively. The reliability of IRTHA is futher validated by statistical analyses, including non-parametric tests (Friedman and Wilcoxon rank-sum tests), convergence curve assessments, and graphical representations with boxplots, which collectively confirm its potential to deliver robust and computationally efficient optimization. From the outcomes, it is observed that the IRTHA demonstrates superior performance compared to other existing MH algorithms. The results obtained by IRTHA show exceptional performance in PV system modeling and parameter estimation in solar PV applications.

This chapter focuses on the application of neural networks to the problem of aircraft aerodynamic modeling and parameter estimation. The neural modeling and neural partial differentiation (NPD) method, which are presented in Chap. 2, are directly applied here to estimate the longitudinal dynamics of an aircraft system. Since the model of an aircraft system is established through the neural network, extends the use of NPD to multi-input and multi-output (MIMO) system ensures online estimation of each aircraft aerodynamics derivatives.

Modelling of energy consumption in electric vehicles is important for operation planning and smart charging strategies. This study proposes an energy consumption modelling and estimation strategy for autonomous electrified shuttles used for public transportation in Linköping. Operational experiences have highlighted the significant influence of weather conditions on the vehicles’ range, and the main complicating factor here is that detailed HVAC system data is unavailable. Therefore, a model is developed for estimating the energy consumption of the shuttles based on a physical formulation of vehicle dynamics and thermodynamics principles under varying weather conditions. The estimation relies on a fusion of predicted meteorological data, such as ambient temperature, and historical shuttle records, including interior temperature, vehicle speed, and discretized State of Charge (SoC). The lack of detailed HVAC operation information, e.g., cooling followed consecutively by heating within the same energy prediction profile, makes energy consumption estimation a challenging task. Even with some dynamic data available specifically for the shuttle’s short waiting time, these challenges render the HVAC system unobservable for nearly identical operations. To overcome this issue, a statistical approach combined with parameter estimation is employed to determine vehicle dynamic parameters and compute the thermal energy consumption and parametrization of the HVAC system. The data-driven model shows a satisfactory fitting level of R 2 with 0.85.

This paper presents a weekly streamflow forecasting model based on linear ARMA (p, q) models, considering both periodic and nonperiodic models. For each week, fifty possible models are automatically analyzed. The best modeling and parameter estimation are chosen based on the minimum square mean forecast error of the whole time series. The proposed model, which has been validated by the Brazilian Multi-Utility Hydrological Studies Working Group, is illustrated in case studies with several hydraulic plants of the Brazilian Southern, Southeastern, North and Northeastern systems.

In this paper, a new panoramic image generation algorithm is proposed based on more realistic image formation processes. Perspective projection, lens distortion, vignetting and illumination effects are incorporated into the proposed panoramic modeling. Intrinsic and extrinsic camera parameters are estimated by the proposed stable camera parameter estimation algorithm derived from panning camera constraints. This paper shows that accurate panoramic images can be reconstructed based on the proposed camera modeling and parameters estimation. The effectiveness of the proposed algorithm is also shown with several image sequences in terms of reconstruction error from the generated panoramic image.

A transfer function type of simplified electrochemical model with modified boundary conditions and Padé approximation for Li-ion battery: Part 2. Modeling and parameter estimation